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Bordallo López, Miguel and Blanco Adán, Carlos Roberto del and García Santos, Narciso (2017). Detecting exercise-induced fatigue using thermal imaging and deep learning. In: "Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), 2017", 28/11/2017 - 01/12/2017, Montreal, Canadá. ISBN 978-1-5386-1842-4. pp. 1-6. https://doi.org/10.1109/IPTA.2017.8310151.
Title: | Detecting exercise-induced fatigue using thermal imaging and deep learning |
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Author/s: |
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Item Type: | Presentation at Congress or Conference (Article) |
Event Title: | Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), 2017 |
Event Dates: | 28/11/2017 - 01/12/2017 |
Event Location: | Montreal, Canadá |
Title of Book: | Proceedings of Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), 2017 |
Título de Revista/Publicación: | PROCEEDINGS OF THE 2017 SEVENTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA 2017) |
Date: | December 2017 |
ISBN: | 978-1-5386-1842-4 |
ISSN: | 2154-512X |
Subjects: | |
Freetext Keywords: | Fatigue detection, facial expression, deep learning, thermal imaging |
Faculty: | E.T.S.I. Telecomunicación (UPM) |
Department: | Señales, Sistemas y Radiocomunicaciones |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
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Fatigue has adverse effects in both physical and cognitive abilities. Hence, automatically detecting exercise-induced fatigue is of importance, especially in order to assist in the planning of effort and resting during exercise sessions. Thermal imaging and facial analysis provide a mean to detect changes in the human body unobtrusively and in variant conditions of pose and illumination. In this context, this paper proposes the automatic detection of exercise-induced fatigue using thermal cameras and facial images, analyzing them using deep convolutional neural networks. Our results indicate that classification of fatigued individuals is possible, obtaining an accuracy that reaches over 80% when utilizing single thermal images.
Type | Code | Acronym | Leader | Title |
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Government of Spain | AEI/FEDER | Unspecified | Unspecified | Unspecified |
Government of Spain | projects TEC2013-48453 (MR-UHDTV) | Unspecified | Unspecified | Unspecified |
Government of Spain | TEC2016-75981 (IVME) | Unspecified | Unspecified | Unspecified |
Item ID: | 50853 |
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DC Identifier: | https://oa.upm.es/50853/ |
OAI Identifier: | oai:oa.upm.es:50853 |
DOI: | 10.1109/IPTA.2017.8310151 |
Official URL: | https://ieeexplore.ieee.org/document/8310151/ |
Deposited by: | Memoria Investigacion |
Deposited on: | 29 May 2018 15:59 |
Last Modified: | 29 May 2018 15:59 |